Cross-session classification of mental workload levels using EEG and an adaptive deep learning model
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The main benefits of separable convolutions are reducing the number of parameters to fit and explicitly decoupling the relationship within and across feature maps.
“The main benefits of separable convolutions are (1) reducing the number of parameters to fit and (2) explicitly decoupling the relationship within and across feature maps”
From EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces (2018) · cites this paper as [42]
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References 44 · Cited by 163
- Authors
- Yin, Z., & Zhang, J.
- Affiliations
- University of Shanghai for Science and Technology (CN)
- East China University of Science and Technology (CN)
- Corresponding author
- Yin, Zhong
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 33
- Pages
- 30-47
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W2549392078
- Cited by
- 163
- References
- 44
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 27 · 2023: 20 · 2022: 19 · 2021: 28 · 2020: 20 · 2019: 18 · 2018: 8 · 2017: 4
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- Funders
- South Dakota Agricultural Experiment Station
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- mental workload classification, EEG, cross-session generalization, stacked denoising autoencoder, adaptive deep learning, online weight tuning, human-machine collaboration, feature selection, noise robustness, data augmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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